Supervised projection with adaptive label assignment for enhanced visualization and chemical process monitoring

Abstract

Data-driven process monitoring methods are widely used in industrial tasks, with visual monitoring enabling operators to intuitively understand operational status, which is vital for maximizing industrial safety and production efficiency. However, high-dimensional industrial data often exhibit complex structures, making the traditional 2D visualization methods ineffective at distinguishing different fault types. Thus, a visual process monitoring method that combines supervised uniform manifold approximation and projection with a label assignment strategy is proposed herein. First, the proposed supervised projection method enhances the visualization step by incorporating label information to guide the nonlinear dimensionality reduction process, improving the degrees of class separation and intraclass compactness. Then, to address the lack of label information for online samples, a label assignment strategy is designed. This strategy integrates kernel Fisher discriminant analysis and Bayesian inference, assigning different label types to online samples based on their confidence levels. Finally, upon integrating the label assignment strategy with the proposed supervised projection method, the assigned labels enhance the separability of online projections and enable the visualization of unknown data to some extent. The proposed method is validated on the Tennessee Eastman process and a real continuous catalytic reforming process, demonstrating superior visual fault monitoring and diagnosis performance to that of the state-of-the-art methods, especially in real industrial applications.